Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells40000
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory144.0 B

Variable types

Numeric8
Unsupported4
Categorical6

Alerts

Exited is highly overall correlated with ComplainHigh correlation
Complain is highly overall correlated with ExitedHigh correlation
Surname has 10000 (100.0%) missing valuesMissing
Geography has 10000 (100.0%) missing valuesMissing
Gender has 10000 (100.0%) missing valuesMissing
Card Type has 10000 (100.0%) missing valuesMissing
RowNumber is uniformly distributedUniform
RowNumber has unique valuesUnique
CustomerId has unique valuesUnique
Surname is an unsupported type, check if it needs cleaning or further analysisUnsupported
Geography is an unsupported type, check if it needs cleaning or further analysisUnsupported
Gender is an unsupported type, check if it needs cleaning or further analysisUnsupported
Card Type is an unsupported type, check if it needs cleaning or further analysisUnsupported
Tenure has 413 (4.1%) zerosZeros
Balance has 3617 (36.2%) zerosZeros

Reproduction

Analysis started2023-06-19 19:31:15.503675
Analysis finished2023-06-19 19:31:26.746279
Duration11.24 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

RowNumber
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-19T15:31:26.989302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2023-06-19T15:31:27.120164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6671 1
 
< 0.1%
6664 1
 
< 0.1%
6665 1
 
< 0.1%
6666 1
 
< 0.1%
6667 1
 
< 0.1%
6668 1
 
< 0.1%
6669 1
 
< 0.1%
6670 1
 
< 0.1%
6672 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%

CustomerId
Real number (ℝ)

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15690941
Minimum15565701
Maximum15815690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-19T15:31:27.253944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15565701
5-th percentile15578824
Q115628528
median15690738
Q315753234
95-th percentile15803034
Maximum15815690
Range249989
Interquartile range (IQR)124705.5

Descriptive statistics

Standard deviation71936.186
Coefficient of variation (CV)0.0045845681
Kurtosis-1.1961125
Mean15690941
Median Absolute Deviation (MAD)62432.5
Skewness0.0011491459
Sum1.5690941 × 1011
Variance5.1748149 × 109
MonotonicityNot monotonic
2023-06-19T15:31:27.383788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15634602 1
 
< 0.1%
15667932 1
 
< 0.1%
15766185 1
 
< 0.1%
15667632 1
 
< 0.1%
15599024 1
 
< 0.1%
15798709 1
 
< 0.1%
15741921 1
 
< 0.1%
15793671 1
 
< 0.1%
15797900 1
 
< 0.1%
15795933 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
15565701 1
< 0.1%
15565706 1
< 0.1%
15565714 1
< 0.1%
15565779 1
< 0.1%
15565796 1
< 0.1%
15565806 1
< 0.1%
15565878 1
< 0.1%
15565879 1
< 0.1%
15565891 1
< 0.1%
15565996 1
< 0.1%
ValueCountFrequency (%)
15815690 1
< 0.1%
15815660 1
< 0.1%
15815656 1
< 0.1%
15815645 1
< 0.1%
15815628 1
< 0.1%
15815626 1
< 0.1%
15815615 1
< 0.1%
15815560 1
< 0.1%
15815552 1
< 0.1%
15815534 1
< 0.1%

Surname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

CreditScore
Real number (ℝ)

Distinct460
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.5288
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-19T15:31:27.517391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile489
Q1584
median652
Q3718
95-th percentile812
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.653299
Coefficient of variation (CV)0.14857651
Kurtosis-0.42572568
Mean650.5288
Median Absolute Deviation (MAD)67
Skewness-0.071606608
Sum6505288
Variance9341.8602
MonotonicityNot monotonic
2023-06-19T15:31:27.766999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 233
 
2.3%
678 63
 
0.6%
655 54
 
0.5%
705 53
 
0.5%
667 53
 
0.5%
684 52
 
0.5%
670 50
 
0.5%
651 50
 
0.5%
683 48
 
0.5%
652 48
 
0.5%
Other values (450) 9296
93.0%
ValueCountFrequency (%)
350 5
0.1%
351 1
 
< 0.1%
358 1
 
< 0.1%
359 1
 
< 0.1%
363 1
 
< 0.1%
365 1
 
< 0.1%
367 1
 
< 0.1%
373 1
 
< 0.1%
376 2
 
< 0.1%
382 1
 
< 0.1%
ValueCountFrequency (%)
850 233
2.3%
849 8
 
0.1%
848 5
 
0.1%
847 6
 
0.1%
846 5
 
0.1%
845 6
 
0.1%
844 7
 
0.1%
843 2
 
< 0.1%
842 7
 
0.1%
841 12
 
0.1%

Geography
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Gender
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Age
Real number (ℝ)

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.9218
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-19T15:31:27.948669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.487806
Coefficient of variation (CV)0.26945841
Kurtosis1.3953471
Mean38.9218
Median Absolute Deviation (MAD)6
Skewness1.0113203
Sum389218
Variance109.99408
MonotonicityNot monotonic
2023-06-19T15:31:28.088668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 478
 
4.8%
38 477
 
4.8%
35 474
 
4.7%
36 456
 
4.6%
34 447
 
4.5%
33 442
 
4.4%
40 432
 
4.3%
39 423
 
4.2%
32 418
 
4.2%
31 404
 
4.0%
Other values (60) 5549
55.5%
ValueCountFrequency (%)
18 22
 
0.2%
19 27
 
0.3%
20 40
 
0.4%
21 53
 
0.5%
22 84
0.8%
23 99
1.0%
24 132
1.3%
25 154
1.5%
26 200
2.0%
27 209
2.1%
ValueCountFrequency (%)
92 2
 
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
84 2
 
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
81 4
< 0.1%
80 3
< 0.1%
79 4
< 0.1%
78 5
0.1%

Tenure
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0128
Minimum0
Maximum10
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-19T15:31:28.219666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8921744
Coefficient of variation (CV)0.57695786
Kurtosis-1.1652252
Mean5.0128
Median Absolute Deviation (MAD)2
Skewness0.010991458
Sum50128
Variance8.3646726
MonotonicityNot monotonic
2023-06-19T15:31:28.320667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1048
10.5%
1 1035
10.3%
7 1028
10.3%
8 1025
10.2%
5 1012
10.1%
3 1009
10.1%
4 989
9.9%
9 984
9.8%
6 967
9.7%
10 490
4.9%
ValueCountFrequency (%)
0 413
 
4.1%
1 1035
10.3%
2 1048
10.5%
3 1009
10.1%
4 989
9.9%
5 1012
10.1%
6 967
9.7%
7 1028
10.3%
8 1025
10.2%
9 984
9.8%
ValueCountFrequency (%)
10 490
4.9%
9 984
9.8%
8 1025
10.2%
7 1028
10.3%
6 967
9.7%
5 1012
10.1%
4 989
9.9%
3 1009
10.1%
2 1048
10.5%
1 1035
10.3%

Balance
Real number (ℝ)

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76485.889
Minimum0
Maximum250898.09
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-19T15:31:28.433666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median97198.54
Q3127644.24
95-th percentile162711.67
Maximum250898.09
Range250898.09
Interquartile range (IQR)127644.24

Descriptive statistics

Standard deviation62397.405
Coefficient of variation (CV)0.81580283
Kurtosis-1.4894118
Mean76485.889
Median Absolute Deviation (MAD)46766.79
Skewness-0.14110871
Sum7.6485889 × 108
Variance3.8934362 × 109
MonotonicityNot monotonic
2023-06-19T15:31:28.561670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
130170.82 2
 
< 0.1%
105473.74 2
 
< 0.1%
85304.27 1
 
< 0.1%
159397.75 1
 
< 0.1%
144238.7 1
 
< 0.1%
112262.84 1
 
< 0.1%
109106.8 1
 
< 0.1%
142147.32 1
 
< 0.1%
109109.33 1
 
< 0.1%
Other values (6372) 6372
63.7%
ValueCountFrequency (%)
0 3617
36.2%
3768.69 1
 
< 0.1%
12459.19 1
 
< 0.1%
14262.8 1
 
< 0.1%
16893.59 1
 
< 0.1%
23503.31 1
 
< 0.1%
24043.45 1
 
< 0.1%
27288.43 1
 
< 0.1%
27517.15 1
 
< 0.1%
27755.97 1
 
< 0.1%
ValueCountFrequency (%)
250898.09 1
< 0.1%
238387.56 1
< 0.1%
222267.63 1
< 0.1%
221532.8 1
< 0.1%
216109.88 1
< 0.1%
214346.96 1
< 0.1%
213146.2 1
< 0.1%
212778.2 1
< 0.1%
212696.32 1
< 0.1%
212692.97 1
< 0.1%

NumOfProducts
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
5084 
2
4590 
3
 
266
4
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Length

2023-06-19T15:31:28.668667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T15:31:28.785667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

HasCrCard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
7055 
0
2945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Length

2023-06-19T15:31:28.878882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T15:31:28.970911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring characters

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

IsActiveMember
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
5151 
0
4849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Length

2023-06-19T15:31:29.052079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T15:31:29.138838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring characters

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

EstimatedSalary
Real number (ℝ)

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100090.24
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-19T15:31:29.236187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9851.8185
Q151002.11
median100193.91
Q3149388.25
95-th percentile190155.38
Maximum199992.48
Range199980.9
Interquartile range (IQR)98386.137

Descriptive statistics

Standard deviation57510.493
Coefficient of variation (CV)0.57458642
Kurtosis-1.1815184
Mean100090.24
Median Absolute Deviation (MAD)49198.15
Skewness0.0020853577
Sum1.0009024 × 109
Variance3.3074568 × 109
MonotonicityNot monotonic
2023-06-19T15:31:29.362667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.92 2
 
< 0.1%
101348.88 1
 
< 0.1%
55313.44 1
 
< 0.1%
72500.68 1
 
< 0.1%
182692.8 1
 
< 0.1%
4993.94 1
 
< 0.1%
124964.82 1
 
< 0.1%
161971.42 1
 
< 0.1%
39488.04 1
 
< 0.1%
187811.71 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
11.58 1
< 0.1%
90.07 1
< 0.1%
91.75 1
< 0.1%
96.27 1
< 0.1%
106.67 1
< 0.1%
123.07 1
< 0.1%
142.81 1
< 0.1%
143.34 1
< 0.1%
178.19 1
< 0.1%
216.27 1
< 0.1%
ValueCountFrequency (%)
199992.48 1
< 0.1%
199970.74 1
< 0.1%
199953.33 1
< 0.1%
199929.17 1
< 0.1%
199909.32 1
< 0.1%
199862.75 1
< 0.1%
199857.47 1
< 0.1%
199841.32 1
< 0.1%
199808.1 1
< 0.1%
199805.63 1
< 0.1%

Exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7962 
1
2038 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Length

2023-06-19T15:31:29.479686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T15:31:29.569684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Complain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7956 
1
2044 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Length

2023-06-19T15:31:29.647684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T15:31:29.737245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
3
2042 
2
2014 
4
2008 
5
2004 
1
1932 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Length

2023-06-19T15:31:29.816518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-19T15:31:30.035031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring characters

ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Card Type
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Point Earned
Real number (ℝ)

Distinct785
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.5151
Minimum119
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-19T15:31:30.153998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile255
Q1410
median605
Q3801
95-th percentile960
Maximum1000
Range881
Interquartile range (IQR)391

Descriptive statistics

Standard deviation225.92484
Coefficient of variation (CV)0.37249664
Kurtosis-1.193781
Mean606.5151
Median Absolute Deviation (MAD)195
Skewness0.008344113
Sum6065151
Variance51042.033
MonotonicityNot monotonic
2023-06-19T15:31:30.287995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
408 26
 
0.3%
709 25
 
0.2%
244 23
 
0.2%
629 23
 
0.2%
503 22
 
0.2%
343 22
 
0.2%
564 22
 
0.2%
351 22
 
0.2%
240 22
 
0.2%
720 21
 
0.2%
Other values (775) 9772
97.7%
ValueCountFrequency (%)
119 1
 
< 0.1%
163 1
 
< 0.1%
206 1
 
< 0.1%
219 16
0.2%
220 7
0.1%
221 14
0.1%
222 11
0.1%
223 12
0.1%
224 9
0.1%
225 14
0.1%
ValueCountFrequency (%)
1000 13
0.1%
999 7
 
0.1%
998 12
0.1%
997 15
0.1%
996 2
 
< 0.1%
995 19
0.2%
994 17
0.2%
993 12
0.1%
992 13
0.1%
991 11
0.1%

Interactions

2023-06-19T15:31:24.974589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:17.374843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:18.594413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:19.542414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:20.500784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:21.649235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:22.565531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:23.543778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:25.255590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:17.533835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:18.709413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:19.656411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:20.672816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:21.759233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:22.681928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:23.669589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:25.470590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:17.711832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:18.828409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:19.769413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:20.789816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:21.874521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:22.797940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:23.809589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:25.596590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:17.828835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:18.943411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:19.879177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:20.902237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:21.979532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:22.908962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:23.959588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:25.752591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:18.075834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:19.066412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:20.005173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:21.023234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:22.100529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:23.027928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:24.123588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:25.890591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:18.187433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:19.180413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:20.121784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:21.266237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:22.209532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:23.143956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:24.469589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:26.001589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:18.314414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:19.297416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:20.235817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:21.386237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:22.324533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:23.264930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:24.655590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:26.125591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:18.478409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:19.428413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:20.366820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:21.516237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:22.455529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:23.395751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-19T15:31:24.831599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-19T15:31:30.403995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
RowNumberCustomerIdCreditScoreAgeTenureBalanceEstimatedSalaryPoint EarnedNumOfProductsHasCrCardIsActiveMemberExitedComplainSatisfaction Score
RowNumber1.0000.0040.0050.000-0.007-0.009-0.0060.0020.0090.0080.0000.0000.0000.010
CustomerId0.0041.0000.0060.009-0.015-0.0140.015-0.0130.0060.0000.0110.0220.0230.000
CreditScore0.0050.0061.000-0.0080.0010.0060.0010.0010.0170.0000.0250.0860.0860.000
Age0.0000.009-0.0081.000-0.0100.033-0.002-0.0010.0870.0130.1440.3750.3730.013
Tenure-0.007-0.0150.001-0.0101.000-0.0100.008-0.0100.0350.0260.0210.0220.0230.008
Balance-0.009-0.0140.0060.033-0.0101.0000.0120.0130.2300.0390.0140.1400.1390.012
EstimatedSalary-0.0060.0150.001-0.0020.0080.0121.000-0.0020.0190.0000.0250.0000.0000.017
Point Earned0.002-0.0130.001-0.001-0.0100.013-0.0021.0000.0000.0000.0000.0000.0070.014
NumOfProducts0.0090.0060.0170.0870.0350.2300.0190.0001.0000.0000.0380.3870.3850.000
HasCrCard0.0080.0000.0000.0130.0260.0390.0000.0000.0001.0000.0060.0000.0000.000
IsActiveMember0.0000.0110.0250.1440.0210.0140.0250.0000.0380.0061.0000.1560.1540.004
Exited0.0000.0220.0860.3750.0220.1400.0000.0000.3870.0000.1561.0000.9950.000
Complain0.0000.0230.0860.3730.0230.1390.0000.0070.3850.0000.1540.9951.0000.000
Satisfaction Score0.0100.0000.0000.0130.0080.0120.0170.0140.0000.0000.0040.0000.0001.000

Missing values

2023-06-19T15:31:26.297623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-19T15:31:26.548030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedComplainSatisfaction ScoreCard TypePoint Earned
0115634602NaN619NaNNaN4220.00111101348.88112NaN464
1215647311NaN608NaNNaN41183807.86101112542.58013NaN456
2315619304NaN502NaNNaN428159660.80310113931.57113NaN377
3415701354NaN699NaNNaN3910.0020093826.63005NaN350
4515737888NaN850NaNNaN432125510.8211179084.10005NaN425
5615574012NaN645NaNNaN448113755.78210149756.71115NaN484
6715592531NaN822NaNNaN5070.0021110062.80002NaN206
7815656148NaN376NaNNaN294115046.74410119346.88112NaN282
8915792365NaN501NaNNaN444142051.0720174940.50003NaN251
91015592389NaN684NaNNaN272134603.8811171725.73003NaN342
RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedComplainSatisfaction ScoreCard TypePoint Earned
9990999115798964NaN714NaNNaN33335016.6011053667.08003NaN791
9991999215769959NaN597NaNNaN53488381.2111069384.71113NaN369
9992999315657105NaN726NaNNaN3620.00110195192.40005NaN560
9993999415569266NaN644NaNNaN287155060.4111029179.52005NaN715
9994999515719294NaN800NaNNaN2920.00200167773.55004NaN311
9995999615606229NaN771NaNNaN3950.0021096270.64001NaN300
9996999715569892NaN516NaNNaN351057369.61111101699.77005NaN771
9997999815584532NaN709NaNNaN3670.0010142085.58113NaN564
9998999915682355NaN772NaNNaN42375075.3121092888.52112NaN339
99991000015628319NaN792NaNNaN284130142.7911038190.78003NaN911